Forecasting

Forecasting and predicting was always an area were humans since ancient years showed special interest and there are examples in all civilizations of either scientifically sound efforts (even by modern standards) or not. We could put in the first category the Antikethyra Mechanism which some researchers say could helped decision makers of the time to organise missions months before since it could calculate with extreme accuracy the movements of the moon and the planets for many years ahead (a major decision-making tool for military expeditions). In the second category were clergies chewing or smoking hallucinogenic leaves or looking over crystal balls. In our days the quest towards forecasting of the first category describes our need for pragmatic realistic relevant and finally accurate predictions. To do so a forecasting methodology must be founded on four basic pylons: The desired level of accuracy must be achievable for the behaviour we are trying to forecast. The forecast must be objective, unbiased and scientific. The forecasters must be trained, skilled experienced and unbiased so they don’t affect forecast accuracy because they are carrying their own agenda. And of course in our technologically advanced era we must use first and for all a suitable software solution that has the necessary capabilities the sound mathematical background and the appropriate methods to provide reliable forecasting. 

Analytical View using SAS forecasting software can help customers analyze and forecast processes that take place over time, identify previously unseen trends and anticipate fluctuations so they can more effectively plan for the future. Whether a customer wants to understand past trends, to forecast the future or better understand how his business functions, we provide a wide range of analytical tools that ensure your success.

Analytical View can help its customers achieve better accuracy and forecasting process efficiency by understanding the nature of demand patterns and where forecasting process is adding value — or not

Areas where forecasting has increased value include (but are not limited to):

  • Supply and demand. How effectively an organization manages its supply chain depends on many factors. An organization can have excellent business processes, yet lack the ability to successfully align supply with demand. Advanced forecasting technology and business intelligence can enhance Sales and Operations Planning while supporting better communications and collaboration throughout an enterprise. Demand Shaping requires the application of multiple technology sets, to understand both supply dynamics and demand dynamics. Combining the analysis of supply and demand, yields a much more complete answer to the issue of their alignment. The ability to quickly identify a misalignment and rapidly develop a remedial response is crucial to the organization’s profitability. Therefore, an organization that develops a demand-shaping competency will have a significant competitive advantage. The core of any attempt to support demand shaping is the set of analytical engines that allow the supply-and-demand dynamics to be explored. The interaction between supply and demand in real-world supply chain systems is complex. This complexity must be modelled using the tools and techniques of mathematics and statistics. Only then can the optimal supply/demand alignment be achieved and maintained.
  • Hospitality. The latest findings show that there are four critical issues that could spell the difference between a hospitality organization thriving or simply surviving. The optimization of customer reward programs; online customer engagement, some myths about forecasting, and creative new approaches in the hospitality field.
  • Utilities. Utility forecasters cannot assume that one methodology will provide the best forecast from one year to the next. To improve forecast performance, reduce uncertainties and generate value in the new data-intensive environment, they must be able to decide which models, or combinations of models, are best. And they must be able to determine more indicators of the factors that affect load. Through SAS solutions utility forecasters can take advantage of hourly or sub-hourly data from millions of smart meters and by using new types of forecasting methodologies, geographic hierarchy and weather station data, they can improve the predictive analytics used to determine future electric usage. 

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